This invention relates generally to imaging procedures, and more particularly to methods and apparatus for improving computer aided detection or diagnosis by utilizing a computer aided processing technique.
Computer aided diagnosis (CAD), such as screening mammography and evaluation of other disease states or medical or physiological events, is typically based upon various types of analysis of a series of collected images. The collected images are analyzed by utilizing the pathologies that are highlighted by a CAD algorithm. The results are generally viewed by radiologists for final diagnosis. As can be appreciated by those skilled in the art, certain subsequent imaging procedures may become feasible or may be recognized as desirable due to the improved management of data volume.
It should be noted that CAD may be utilized in any imaging modality, such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray systems, ultrasound systems, positron emission tomography (PET), and so forth. CAD algorithms in certain of these modalities may provide advantages over those in other modalities, depending upon the imaging capabilities of the modality, the tissue being imaged, and so forth. Computed tomography, for example, is generally a diagnostic procedure in which cross-sectional images or slices are made by an X-ray system. The CT scanning procedure combines the use of a computer system and a rotating X-ray device to create detailed cross sectional images or “slices” of a patient's organs and other body parts. The imaging capabilities are physically similar to those of X-ray systems. MRI, ultrasound, PET, and other modalities similarly are adapted to imaging certain tissues or anatomies, and provide advantages for the different CAD algorithm employed with images they produce.
Each imaging modality is based upon unique physics and image processing techniques. For example, a CT system measures the attenuation of X-ray beams passed through a patient from numerous angles, and then, based upon these measurements, a computer is able to reconstruct images of the portions of a patient's body responsible for the radiation attenuation. As will be appreciated by those skilled in the art, these images are based upon separate examination of a series of continuous cross sections. Thus, a virtual 3-D image may be produced by a CT examination. It should be pointed out that a CT system does not actually directly provide an image, but rather numerical values of tissue density. The image based upon the reconstructed data is typically displayed on a cathode ray tube, and may be printed or reproduced on film.
Continuing with the example of CT imaging, CT scanners operate by projecting fan shaped X-ray beams from an X-ray source that is collimated and passes through the object, such as a patient, that is then detected by a detector element. The data is then used to produce a useful image. Thus, the detector element produces data based on the attenuation of the X-ray beams, and the data are processed by computer analysis. The locations of pathologies may then be highlighted by the CAD algorithm, and thus brought to a human observer's attention. A radiologist or other physician for final diagnosis may then review the results.
Each imaging modality may provide unique advantages over other modalities for certain types of disease or physiological condition detection. For example, CT scanning provides advantages over other types of techniques in diagnosing disease particularly because it illustrates the shape and exact location of organs, soft tissues, and bones for any slice of the body. Further, CT scans may help doctors distinguish between a simple cyst, for example, and a solid tumor, and thus evaluate abnormalities more accurately. As mentioned above, other imaging modalities are similarly best suited to imaging other physiological features of interest, and to corresponding CAD algorithms.
Existing techniques for computerized diagnosis of physiological features suffer from certain drawbacks. For example, the output of the CAD analysis is generally fairly, interactive, requiring assessment and evaluation by a seasoned practitioner. Due to time constraints and the availability of such persons, a patient is often called upon to report for certain types of examination, with further examinations needing to be scheduled, when appropriate, based upon the review of the CAD analysis. That is to say, patients often must return for additional tests on the same or a different modality imaging system in order to properly evaluate and diagnose potential conditions. The resulting procedure is not only time-consuming for the patient and for the physician, but ultimately results in the entire process extending over a considerable period of time. Additional appointments for subsequent imaging can also result in considerable expense both for the patient, for hospitals and clinics, and for insurance carriers.
For example, thin slice, high-resolution, CT (HRCT) scanner technology generates magnitudes of axial and volumetric data that requires significant time for radiologists to review. This demanding of more time from the radiologist may lessen the number of exams he or she can complete on a daily basis. Additionally, the radiologist's responsibility for high sensitivity to a vast amount of information presented in HRCT images may be threatening and may even discourage radiologists from performing screening (or therapy follow-up) studies in the first place. An answer to this explosion of data and patient management has been computer-assisted detection (CAD) of features of interests (FOIs) within image volumes. As a second-reviewer (complementing the initial radiologist review) CAD provides assistance to radiologists by setting markers where gray-levels in the CT image are unexpected, match a distinctive pattern, or do not appear as might be typically expected in a healthy individual.
Whether a FOI is detected by a CAD system or by a radiologist (or the combination of both), the critical step toward informed clinical management of that feature is in accurate segmentation (from other anatomic or pathologic structures) and quantification (volumetric, densitometric, functional, geometric, etc.). Since the release of applications such as Advanced Lung Analysis (ALA), it has been learned that the ability to accurately determine the volumetric size of small objects depends on the scan-acquisition and reconstruction variables used in generating an image volume. Considerable variability in segmentation and sizing of small features may be introduced due to partial-volume effects inherent to multi-slice CT scanner acquisition, patient motion, and mis-registration. Therefore, it is advisable to perform a targeted reconstruction at a small display field of view and optimal reconstruction parameters to capture maximum detail from a detected FOI. Unfortunately, at the time the radiologist reviews (and detects) a FOI in a typical screening exam, the raw projection (scan) data has been overwritten or removed from the CT console and thus, retrospective acquisition of projection data is no longer an option. Additionally, sometimes the CAD analysis results in a false positive.